code: def get_peft_config_as_dict(self, inference: bool = False):
      config_dict = {}
      for key, value in self.peft_config.items():
          config = {
              k: v.value if isinstance(v, Enum) else v
              for k, v in asdict(value).items()
          }
          if inference:
              config["inference_mode"] = True
      config_dict[key] = config
      return config
  
imports: from contextlib import contextmanager
  from dataclasses import asdict
  from enum import Enum
  from typing import Any
  import torch
  from torch import nn
  from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists
  from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING, ModulesToSaveWrapper
  from .config import PolyConfig
  from .layer import Linear, PolyLayer
label_desc: Use image node_id 8 for calling the PolyModel obj's underlying member method code with example usage: obj.get_peft_config_as_dict(inference) and returns: config